Polysome-profiling in small tissue samples - proof of concept experiment for the use of an optimized non linear gradient

Polysome-profiling is commonly used to study translatomes, i.e. transcriptome-wide patterns of translational efficiency. The standard approach for collecting efficiently translated polysome-associated RNA results in laborious extraction of RNA from a large volume across many fractions. This property makes polysome-profiling inconvenient for larger experimental designs or samples with low RNA amounts such as primary cells or frozen tissues. To address this, we optimized a non-linear sucrose gradient which reproducibly enriches for mRNAs associated with >3 ribosomes in only one or two fractions, thereby reducing sample handling 5-10 fold. The technique can be applied to frozen tissue samples from biobanks, and generates polysome-associated RNA with a quality reflecting the starting material. When coupled with smart-seq2, a single-cell RNA sequencing technique, translatomes from small tissue samples can be obtained. Translatomes acquired using optimized non-linear gradients resemble those obtained with the standard approach employing linear gradients. Polysome-profiling using optimized non-linear gradients in serum starved HCT-116 cells with or without p53 showed that p53 status associated with changes in mRNA abundance and translational efficiency leading to changes in protein levels. Moreover, p53 status also induced translational buffering whereby changes in mRNA levels are buffered at the level of mRNA translation to maintain protein levels constant. Thus, here we present a polysome-profiling technique applicable to large study designs, primary cells and frozen tissue samples such as those collected in bio banks. Overall design: We used two cell lines that differ in their p53 status (HCT-116 p53+/+ and HCT-116 p53-/-) and serum-starved them (16 h). We prepared cytosolic lysates from 6 plates (15 cm) from each cell type and divided the lysates equally between the optimized non-linear gradient and the linear gradient groups. We then collected fractions corresponding to mRNA associated with >3 ribosomes from the linear gradient, peak 0 and +1 from the optimized non-linear gradient as well as the cytosolic input. We repeated the experiment four times.